We use Adaptive Moment Estimation (ADAM) [1]} as an optimizer for updating the network weights in CML, FL, and LML. Additionally, we apply an initial learning rate \(\eta \)  of \(0.01\) , which is reduced by \(50\%\)  every five epochs.
Lastly, we use ES via monitoring the loss or F1-score on the validation set with patience \(P=30\)  and the maximum number of epochs set to 100. In case the F1-score is undefined, i.e., recall and precision are zero, we set it to \(-1\) . The local minibatch size \(B\)  depends on the number of clients \(K\)  participating in each configuration: we use \(B=512/K\)  for performance reasons.
For FL, we use \(E=1\)  number of local epochs. The fraction of clients computing in each FL round is \(C=1\) , meaning that all clients participate in each iteration.
